Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices
Authors: Nathaniel Cohen, Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we demonstrate Slicedit s ability to edit a wide range of real-world videos, confirming its clear advantages compared to existing competing methods. |
| Researcher Affiliation | Academia | 1Mines Paris PSL Research University, Paris, France 2Technion Israel Institute of Technology, Haifa, Israel. |
| Pseudocode | Yes | H.2. Slicedit: The resulting algorithm, Alg. 3, given with the notations from the main paper is as follows |
| Open Source Code | No | The paper mentions that 'competing methods' code is publicly available, but does not provide an explicit statement or link for its own source code. |
| Open Datasets | Yes | We evaluate our method on a dataset of videos, which we collected from the DAVIS dataset (Pont-Tuset et al., 2017), the LOVEU-TGVE dataset (Wu et al., 2023b) and from the internet. |
| Dataset Splits | No | The paper describes the dataset used but does not specify training, validation, or test splits for it. |
| Hardware Specification | Yes | On a single RTX A6000 GPU, the one we used for running all methods including ours, these methods could edit videos of only up to 30 frames. |
| Software Dependencies | No | The paper mentions 'Stable Diffusion v2.12' and 'RIFE', but does not provide specific version numbers for software dependencies like programming languages or libraries (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | We set the classifier free guidance (Ho & Salimans, 2021) strength parameter to 10 in ĻµEA and to 1 in ĻµS. Moreover, we inject the extended attention features from the source video to the target video in 85% of the sampling process. We set Ī³, the balancing parameter in Eq. (1), to 0.8. |